Mini-Project 02

Data Visualization Project 02

revised version of mini-project 02 goes here

Filtering and Summarizing

Including tidyverse, sf, and plotly libraries. Assigning dataset, and adjusting column names for ease of creating plots later.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(sf)

## Linking to GEOS 3.9.1, GDAL 3.3.2, PROJ 7.2.1; sf_use_s2() is TRUE

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

housing_data <- read_csv("https://raw.githubusercontent.com/reisanar/datasets/master/WestRoxbury.csv")

## Rows: 5802 Columns: 14

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): REMODEL
## dbl (13): TOTAL VALUE, TAX, LOT SQFT, YR BUILT, GROSS AREA, LIVING AREA, FLO...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

names(housing_data) <- make.names(names(housing_data), unique=TRUE)
housing_data

## # A tibble: 5,802 × 14
##    TOTAL.VALUE   TAX LOT.SQFT YR.BUILT GROSS.AREA LIVING.AREA FLOORS ROOMS
##          <dbl> <dbl>    <dbl>    <dbl>      <dbl>       <dbl>  <dbl> <dbl>
##  1        344.  4330     9965     1880       2436        1352      2     6
##  2        413.  5190     6590     1945       3108        1976      2    10
##  3        330.  4152     7500     1890       2294        1371      2     8
##  4        499.  6272    13773     1957       5032        2608      1     9
##  5        332.  4170     5000     1910       2370        1438      2     7
##  6        337.  4244     5142     1950       2124        1060      1     6
##  7        359.  4521     5000     1954       3220        1916      2     7
##  8        320.  4030    10000     1950       2208        1200      1     6
##  9        334.  4195     6835     1958       2582        1092      1     5
## 10        409.  5150     5093     1900       4818        2992      2     8
## # … with 5,792 more rows, and 6 more variables: BEDROOMS <dbl>,
## #   FULL.BATH <dbl>, HALF.BATH <dbl>, KITCHEN <dbl>, FIREPLACE <dbl>,
## #   REMODEL <chr>

Assigning florida lakes shapefile data.

florida_lakes <- read_sf("data/Florida_Lakes/Florida_Lakes.shp")
florida_lakes

## Simple feature collection with 4243 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.42774 ymin: 25.02625 xmax: -80.03097 ymax: 31.00254
## Geodetic CRS:  WGS 84
## # A tibble: 4,243 × 7
##    PERIMETER NAME   COUNTY OBJECTID SHAPEAREA SHAPELEN                  geometry
##        <dbl> <chr>  <chr>     <int>     <dbl>    <dbl>        <MULTIPOLYGON [°]>
##  1    11082. Lake … ORANGE        1  1818000.   11082. (((-81.34813 28.62354, -…
##  2     2834. Black… ESCAM…        2    31380.    2834. (((-87.42029 30.49087, -…
##  3    18768. Lake … HIGHL…        3 13601177.   18768. (((-81.4614 27.46472, -8…
##  4      493. Halfm… ESCAM…        4     6337.     493. (((-87.3131 30.74034, -8…
##  5     5663. Cresc… ESCAM…        5   338242.    5663. (((-87.27591 30.4692, -8…
##  6      317. Black… SANTA…        6     2380.     317. (((-87.26869 30.69546, -…
##  7      181. Beave… ESCAM…        7     1381.     181. (((-87.27064 30.70558, -…
##  8     1376. Salte… ESCAM…        8    24421.    1376. (((-87.26273 30.94937, -…
##  9     1914. Forty… SANTA…        9   178663.    1914. (((-87.18693 30.81357, -…
## 10      328. Hutso… SANTA…       10     7838.     328. (((-87.14079 30.96851, -…
## # … with 4,233 more rows

Assigning florida state boundary shapfile. Data source.

florida_shape <- read_sf("data/Detailed_Florida_State_Boundary/Detailed_Florida_State_Boundary.shp")
florida_shape

## Simple feature collection with 1 feature and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.63493 ymin: 24.52104 xmax: -80.03132 ymax: 31.0002
## Geodetic CRS:  WGS 84
## # A tibble: 1 × 5
##   OBJECTID    ID   Shape_STAr Shape_STLe                                geometry
##      <int> <int>        <dbl>      <dbl>                      <MULTIPOLYGON [°]>
## 1        1     1 145697142592   7614166. (((-85.54423 30.00015, -85.54407 29.99…

Filtering lakes dataset to only orange county for ease of handling and further exploration.

florida_lakes_orange = filter(florida_lakes, COUNTY %in% c("ORANGE"))
florida_lakes_orange

## Simple feature collection with 365 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -81.69661 ymin: 28.34709 xmax: -80.95306 ymax: 28.78791
## Geodetic CRS:  WGS 84
## # A tibble: 365 × 7
##    PERIMETER NAME   COUNTY OBJECTID SHAPEAREA SHAPELEN                  geometry
##  *     <dbl> <chr>  <chr>     <int>     <dbl>    <dbl>        <MULTIPOLYGON [°]>
##  1    11082. Lake … ORANGE        1  1818000.   11082. (((-81.34813 28.62354, -…
##  2    13601. Lake … ORANGE       69  3680008.   13601. (((-81.51848 28.49421, -…
##  3    18261. Big S… ORANGE       70  4131164.   18261. (((-81.48132 28.40898, -…
##  4      979. Lake … ORANGE      324    66933.     979. (((-81.50882 28.60936, -…
##  5      492. Lake … ORANGE      329    16645.     492. (((-81.49032 28.58921, -…
##  6     6288. Crook… ORANGE      330   433676.    6288. (((-81.48208 28.5978, -8…
##  7      547. Carp … ORANGE      331    19872.     547. (((-81.50163 28.65873, -…
##  8     1193. Lake … ORANGE      332    98047.    1193. (((-81.53446 28.55185, -…
##  9     1727. Lake … ORANGE      334   227921.    1727. (((-81.5 28.39675, -81.4…
## 10     1048. Geyer… ORANGE      337    67286.    1048. (((-81.48597 28.53726, -…
## # … with 355 more rows

Generating scatterplot to explore data and find outliers. I used plotly to identify data points that were outliers.

perimeter_area <- ggplot(florida_lakes_orange, aes(x = PERIMETER, y = SHAPEAREA, text = NAME)) + geom_point() 
ggplotly(perimeter_area, tooltip = "text")

Filtering outliers out of dataset

florida_lakes_orange_outlierless = filter(florida_lakes_orange, !(NAME %in% c("Lake Apopka", "Johns Lake")))

Creating a model to compare lake perimeter to area.

perimeter_vs_area <- lm(PERIMETER ~ SHAPEAREA, data = florida_lakes_orange_outlierless)
summary(perimeter_vs_area)

## 
## Call:
## lm(formula = PERIMETER ~ SHAPEAREA, data = florida_lakes_orange_outlierless)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7966.6  -605.5  -292.0   206.3  6973.8 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.222e+03  7.440e+01   16.42   <2e-16 ***
## SHAPEAREA   3.357e-03  8.449e-05   39.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1307 on 361 degrees of freedom
## Multiple R-squared:  0.8139, Adjusted R-squared:  0.8133 
## F-statistic:  1578 on 1 and 361 DF,  p-value: < 2.2e-16

library(broom)
tidy(perimeter_vs_area)

## # A tibble: 2 × 5
##   term          estimate  std.error statistic   p.value
##   <chr>            <dbl>      <dbl>     <dbl>     <dbl>
## 1 (Intercept) 1222.      74.4            16.4 1.23e- 45
## 2 SHAPEAREA      0.00336  0.0000845      39.7 7.51e-134

glance(perimeter_vs_area)

## # A tibble: 1 × 12
##   r.squared adj.r.squared sigma statistic   p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>     <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1     0.814         0.813 1307.     1578. 7.51e-134     1 -3119. 6243. 6255.
## # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

Visualizing

Creating an interactive plot to compare total value, gross area, and number of rooms for West Roxbury data.

housing_plot <- ggplot(housing_data, aes(x = GROSS.AREA, y = TOTAL.VALUE, color = as.factor(ROOMS))) + geom_point() + 
  theme(plot.title.position = "plot") + 
  labs(title = "Total Value of Homes vs Total Area",
       subtitle = "for houses in the West Roxbury neighborhood", 
       color = "Number of Rooms") + 
  xlab("Total Area") + 
  ylab("Total Value")
ggplotly(housing_plot)

Creating an interactive map of the lakes of Florida, using the Florida Lakes shapefile data, and detailed state boundary shapfile data.

lake_plot <- ggplot() +
  geom_sf(color = "#70DB92", fill = "#98F5B4", data = florida_shape) + theme(legend.position = "none") + theme_classic() + geom_sf(color = "#54A4F0", fill = "#498FD1", data = florida_lakes, aes(text = NAME)) + 
  theme(plot.title.position = "plot") + 
  labs(title = "Lakes in the State of Florida",
       subtitle = "hover over each lake to see its name")

## Warning: Ignoring unknown aesthetics: text

ggplotly(lake_plot, tooltip = "text")

Creating a plot based on modeling perimeter vs area for lakes in Orange county.

perimeter_area_outlierless <- ggplot(florida_lakes_orange_outlierless, aes(x = PERIMETER, y = SHAPEAREA)) + geom_point() + geom_smooth(method = lm) + 
  theme(plot.title.position = "plot") + 
  labs(title = "Model of Perimeter vs Area of lakes in Orange County FL",
       subtitle = "excluding Lake Apopka and Johns Lake") + 
  xlab("Perimeter") + 
  ylab("Area")
ggplotly(perimeter_area_outlierless)

## `geom_smooth()` using formula 'y ~ x'

perimeter_area <- ggplot(florida_lakes, aes(x = PERIMETER, y = SHAPEAREA)) + geom_hex() + geom_smooth(method = lm) + 
  theme(plot.title.position = "plot") + 
  labs(title = "Model of Perimeter vs Area of lakes in Florida") + 
  xlab("Perimeter") + 
  ylab("Area") + 
  ylim(0, 200000000) + 
  xlim(0, 150000)
ggplotly(perimeter_area)

## Warning: Removed 6 rows containing non-finite values (stat_binhex).

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

lake_plot

Analyzing

Originally I was hoping to focus more on the Florida lakes data, and combine it with fish population data to create my visualizations, however I was not able to find any fish population data for lakes in Florida that would work to create the visualizations as I had originally intended. Instead, I used the West Roxbury data for my first interactive plot, and had to settle on just creating an interactive map that displayed lake names for my shapefile plot. There were not many steps involved in cleaning the data for this project. The only absolutely necessary step was to remove the spaces from the column names in the West Roxbury set so that I could create visualizations. Other than that I filtered the lake data to make it more managable and more easily readable to create the scatterplot and model.

My first visualization could be used to tell a story about the leading factors that affect price for homes. However, I think that my second two plots could be used well together to explore how lakes have formed in Florida, as well as some of the underlying geometric principles that dictate that. The map plot would provide a good visual reference for exploring how lakes have formed in different regions of the state of Florida. The linear model could be used to explain how to estimate the area of those lakes by only measuring their perimeter, which would be much easier to do than trying to actually measure the area of a body of water. The main difficulty I ran into was in converting the column names in the West Roxbury dataset. I think that I would further exploring the lake data by incorporating additional data as I previously discussed would be a great way to further explore that dataset.

I continued to focus on creating visualizations that are easy to read and approachable, as well as maintaining a clean style. For the map I created I used Adobe Color to find complementary colors to represent the land and water area of Florida, as well as their outlines. I tried to keep labels simple but informative, and minimize wasteful elements.

For this revised version, I kept most of this project the same, however I decided to expand my analysis of the perimeter - area model. I included a version of the chart that includes the majority of the data, while showing a similar relationship as that of only orange county. For this visualization I utilized a hex bin geometry as per the recommendation of Dr. Sanchez.